A serverless vector database
built from first principles on object storage: 10-100x cheaper, usage-based pricing, massive scalability
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║ client ║░───API──▶║ ┃ Memory/ ┃────▶┃ Object ┃ ║░
║ ║░ ║ ┃ SSD Cache ┃ ┃ Storage (S3) ┃ ║░
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╔═turbopuffer══════════════════╗
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║ ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ ║░
║ ┃ Memory/ ┃ ║░
║ ┃ SSD Cache ┃ ║░
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║ ┃Object storage (S3)┃ ║░
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768
| Items | Unit cost | Quantity | Total |
|---|---|---|---|
Storage | $1.00 | ||
Writes | $1.00 | ||
Queries | $0.00 |
Estimated cost
$2.00
per month
Query latency
Warm query: 1m vectors
Percentile
Latency
P50
28ms
P90
37ms
P99
63ms
MAX
98ms
Warm queries have all their data in cache.
Search for 100 random vectors from the dataset with top k = 10, when dataset is fully in cache. Warming the cache for an index takes about ~10s (for 1m vectors) after the first cold query, and typically stays in cache for a few hours.
Hear from our customers
“
After switching our vector db to @turbopuffer, we're saving an order of magnitude in costs and dealing with far less complexity!
Aman Sanger
Co-founder
“
Moving to turbopuffer felt less like an upgrade and more like discovering a new paradigm. We didn’t just save costs; we turned once-prohibitive features into standard tools in our arsenal. Vectorize all the things and say goodbye to sharding
Justin Watts
Distinguished Engineer
Limits
| Metric | Max seen in production | Production limits (current) | Production limits (soon) |
|---|---|---|---|
| Max vectors (global) | 5B+ | ||
| Max vectors (per namespace) | 100M+ | ||
| Number of namespaces | 2M+ | Unlimited | |
| Max dimensions | 10,752 | ||
| Max inactive time in cache | Contact us if you need custom | ||
| Write rate (global) | 10,000 | ||
| Write rate (per namespace) | 10,000 | 10,000 vec/s | 100,000 vec/s |
| Max write batch rate (per namespace) | 1 batch/s | ||
| QPS (global) | ~60 QPS | ||
| Max QPS (per namespace) | ~20 QPS | 100+ QPS | 10,000 QPS |
| ~90-95% | ~90-95% | Configurable | |
| See full list |
FAQ
